HSCP - 9-digit code for Health and Social Care Partnerships (2016) of residence

HB - 9-digit code for health board of treatment based on boundaries as at 1st April 2019

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
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## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
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## x dplyr::lag()    masks stats::lag()
library(here)
## Warning: package 'here' was built under R version 4.1.3
## here() starts at C:/Users/mahri/OneDrive/CodeClan/rshiny_dashboard_project/Work In Progress/Demographics
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.3
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(ggplot2)
library(lubridate)
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## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union
library(stringr)


Read In Data

covid_admissions_HB_agesex <- read_csv(here("../../raw_data/covid_data/hospital_admissions_hb_agesex_20220302.csv"))
## Rows: 43516 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (8): HB, HBQF, AgeGroup, AgeGroupQF, Sex, SexQF, AdmissionType, Admissio...
## dbl (4): WeekEnding, NumberAdmissions, Average20182019, PercentVariation
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
covid_admissions_HB_simd <- read_csv(here("../../raw_data/covid_data/hospital_admissions_hb_simd_20220302.csv"))
## Rows: 21138 Columns: 9
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): HB, HBQF, AdmissionType, AdmissionTypeQF
## dbl (5): WeekEnding, SIMDQuintile, NumberAdmissions, Average20182019, Percen...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Clean Names

covid_admissions_HB_agesex <- janitor::clean_names(covid_admissions_HB_agesex)
covid_admissions_HB_simd <- janitor::clean_names(covid_admissions_HB_simd)

Change week_ending column to a date and creating new month, year AND a month and year col

# Health Board x Age sex
covid_admissions_HB_agesex <- covid_admissions_HB_agesex %>%
  mutate(week_ending = ymd(week_ending))

covid_admissions_HB_agesex <- covid_admissions_HB_agesex %>%
  mutate(
    wk_ending_month = month(week_ending, label = TRUE, abbr = FALSE),
    wk_ending_year = year(week_ending)
  ) %>% 
  unite(wk_ending_yr_month, 
        wk_ending_month, wk_ending_year, 
        remove = FALSE, 
        sep = " ")
covid_admissions_HB_agesex
#Health board x simd
covid_admissions_HB_simd <- covid_admissions_HB_simd %>%
  mutate(week_ending = ymd(week_ending))

covid_admissions_HB_simd <- covid_admissions_HB_simd %>%
  mutate(
    wk_ending_month = month(week_ending, label = TRUE, abbr = FALSE),
    wk_ending_year = year(week_ending)
  ) %>% 
  unite(wk_ending_yr_month, 
        wk_ending_month, wk_ending_year, 
        remove = FALSE, 
        sep = " ")
covid_admissions_HB_simd


Adding a quarter and year column to both

covid_admissions_HB_agesex <- covid_admissions_HB_agesex %>% 
  mutate(year_quarter = case_when(
    wk_ending_yr_month == "January 2020" ~ "2020Q1",
    wk_ending_yr_month == "February 2020" ~ "2020Q1",
    wk_ending_yr_month == "March 2020" ~ "2020Q1",
    wk_ending_yr_month == "April 2020" ~ "2020Q2",
    wk_ending_yr_month == "May 2020" ~ "2020Q2",
    wk_ending_yr_month == "June 2020" ~ "2020Q2",
    wk_ending_yr_month == "July 2020" ~ "2020Q3",
    wk_ending_yr_month == "August 2020" ~ "2020Q3",
    wk_ending_yr_month == "September 2020" ~ "2020Q3",
    wk_ending_yr_month == "October 2020" ~ "2020Q4",
    wk_ending_yr_month == "November 2020" ~ "2020Q4",
    wk_ending_yr_month == "December 2020" ~ "2020Q4",
    wk_ending_yr_month == "January 2021" ~ "2021Q1",
    wk_ending_yr_month == "February 2021" ~ "2021Q1",
    wk_ending_yr_month == "March 2021" ~ "2021Q1",
    wk_ending_yr_month == "April 2021" ~ "2021Q2",
    wk_ending_yr_month == "May 2021" ~ "2021Q2",
    wk_ending_yr_month == "June 2021" ~ "2021Q2",
    wk_ending_yr_month == "July 2021" ~ "2021Q3",
    wk_ending_yr_month == "August 2021" ~ "2021Q3",
    wk_ending_yr_month == "September 2021" ~ "2021Q3",
    wk_ending_yr_month == "October 2021" ~ "2021Q4",
    wk_ending_yr_month == "November 2021" ~ "2021Q4",
    wk_ending_yr_month == "December 2021" ~ "2021Q4",
    wk_ending_yr_month == "January 2022" ~ "2022Q1",
    wk_ending_yr_month == "February 2022" ~ "2022Q1",
  ))

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  mutate(year_quarter = case_when(
    wk_ending_yr_month == "January 2020" ~ "2020Q1",
    wk_ending_yr_month == "February 2020" ~ "2020Q1",
    wk_ending_yr_month == "March 2020" ~ "2020Q1",
    wk_ending_yr_month == "April 2020" ~ "2020Q2",
    wk_ending_yr_month == "May 2020" ~ "2020Q2",
    wk_ending_yr_month == "June 2020" ~ "2020Q2",
    wk_ending_yr_month == "July 2020" ~ "2020Q3",
    wk_ending_yr_month == "August 2020" ~ "2020Q3",
    wk_ending_yr_month == "September 2020" ~ "2020Q3",
    wk_ending_yr_month == "October 2020" ~ "2020Q4",
    wk_ending_yr_month == "November 2020" ~ "2020Q4",
    wk_ending_yr_month == "December 2020" ~ "2020Q4",
    wk_ending_yr_month == "January 2021" ~ "2021Q1",
    wk_ending_yr_month == "February 2021" ~ "2021Q1",
    wk_ending_yr_month == "March 2021" ~ "2021Q1",
    wk_ending_yr_month == "April 2021" ~ "2021Q2",
    wk_ending_yr_month == "May 2021" ~ "2021Q2",
    wk_ending_yr_month == "June 2021" ~ "2021Q2",
    wk_ending_yr_month == "July 2021" ~ "2021Q3",
    wk_ending_yr_month == "August 2021" ~ "2021Q3",
    wk_ending_yr_month == "September 2021" ~ "2021Q3",
    wk_ending_yr_month == "October 2021" ~ "2021Q4",
    wk_ending_yr_month == "November 2021" ~ "2021Q4",
    wk_ending_yr_month == "December 2021" ~ "2021Q4",
    wk_ending_yr_month == "January 2022" ~ "2022Q1",
    wk_ending_yr_month == "February 2022" ~ "2022Q1",
  ))



What are we working with?

Looking to see if i can join data? Probably??? All the HSPC corresponding health board values are S08000015 - s08000032 https://www.opendata.nhs.scot/dataset/geography-codes-and-labels/resource/944765d7-d0d9-46a0-b377-abb3de51d08e

The HB data set is all S08000015 - s08000032

covid_admissions_HB_agesex %>% 
  distinct(hb)

We only need ACUTE patients:

admission_type has: All, Emergency and Planned * all and emergency always have similar figures compared with planned.

covid_admissions_HB_agesex %>% 
  group_by(admission_type) %>% 
  summarise(total = n())
covid_admissions_HB_agesex %>% 
  group_by(age_group) %>% 
  summarise(total = n())
covid_admissions_HB_simd%>% 
  group_by(admission_type) %>% 
  summarise(total = n())
covid_admissions_HB_simd %>% 
  group_by(simd_quintile) %>% 
  summarise(total = n())
covid_admissions_HB_agesex <- covid_admissions_HB_agesex %>% 
  filter(admission_type == "Emergency")

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  filter(admission_type == "Emergency")

ANd we don’t need ALL ages either… edit: yes we do: * The “sex” column “Male, Female, All” - m & f only come under “All ages”!

covid_admissions_HB_AGE <- covid_admissions_HB_agesex %>% 
  filter(age_group != "All ages")


HEALTH BOARD - sex and age

HB - Age - Total Covid admissions


Firstly need a total column for each age group per month and for equivalent average18/19 - MONTH AND QUARTER:

covid_admissions_HB_AGE <- covid_admissions_HB_AGE %>% 
  group_by(age_group, wk_ending_yr_month) %>% 
  mutate(total_admissions_per_month_age = sum(number_admissions))

covid_admissions_HB_AGE <- covid_admissions_HB_AGE %>% 
  group_by(age_group, wk_ending_yr_month) %>% 
  mutate(admissions_2018_19_per_month_age = sum(average20182019))
covid_admissions_HB_AGE
covid_admissions_HB_AGE <- covid_admissions_HB_AGE %>% 
  group_by(age_group, year_quarter) %>% 
  mutate(total_admissions_per_quarter_age = sum(number_admissions))
covid_admissions_HB_AGE
covid_admissions_HB_AGE <- covid_admissions_HB_AGE %>%
  group_by(age_group, year_quarter) %>% 
  mutate(admissions_2018_19_per_quarter_age = sum(average20182019))
covid_admissions_HB_AGE

April 2020 will be shown first and ages are a mess. So:

dates <- c("January 2020", "February 2020", "March 2020", "April 2020", 
           "May 2020", "June 2020", "July 2020", "August 2020", 
           "September 2020", "October 2020", "November 2020", "December 2020", 
           "January 2021", "February 2021", "March 2021", "April 2021", 
           "May 2021", "June 2021", "July 2021", "August 2021", 
           "September 2021", "October 2021", "November 2021", "December 2021", 
           "January 2022", "February 2022")

summer_dates <- c("April 2020", "May 2020", "June 2020", "July 2020", 
                  "August 2020", "September 2020", "April 2021", "May 2021", 
                  "June 2021", "July 2021", "August 2021", "September 2021")

winter_dates <- c("January 2020", "February 2020", "March 2020", "October 2020",
                  "November 2020", "December 2020", "January 2021", 
                  "February 2021", "March 2021", "October 2021", 
                  "November 2021", "December 2021", "January 2022", 
                  "February 2022")

quarter_dates <- c("2016Q3", "2016Q4", "2017Q1", "2017Q2", "2017Q3", "2017Q4", 
                   "2018Q1", "2018Q2", "2018Q3", "2018Q4", "2019Q1", "2019Q2", 
                   "2019Q3", "2019Q4", "2020Q1", "2020Q2", "2020Q3", "2020Q4",
                   "2021Q1", "2021Q2", "2021Q3")

q1 <- c("January 2020", "February 2020", "March 2020", "January 2021", 
        "February 2021", "March 2021", "January 2022", "February 2022")

q2 <- c("April 2020", "May 2020", "June 2020", "April 2021", "May 2021", 
        "June 2021") 

q3 <- c("July 2020", "August 2020", "September 2020", "July 2021", 
        "August 2021", "September 2021")

q4 <- c("October 2020", "November 2020", "December 2020", "October 2021", 
        "November 2021", "December 2021")


PLOT OF Total COVID admissions per month by age group

covid_admissions_HB_AGE %>% 
  mutate(age_group = fct_relevel(age_group, 
                                 "Under 5", "5 - 14", "15 - 44", 
                                 "45 - 64", "65 - 74", "75 - 84", 
                                 "85 and over")) %>%
  ggplot()+
  aes(x = wk_ending_yr_month, 
      y = total_admissions_per_month_age, 
      group = age_group, 
      colour = age_group)+
  scale_x_discrete(limits = dates) +
  geom_point()+
  geom_line()+
  labs(x = "Month and Year",
       y = "Total admissions",
       title = "(HB) Total Admissions per month across Scotland by age group",
       subtitle = "January, 2020 - February 2022",
       colour = "Age Group") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
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By Quarter:

covid_admissions_HB_AGE %>% 
  mutate(age_group = fct_relevel(age_group, 
                                 "Under 5", "5 - 14", "15 - 44", 
                                 "45 - 64", "65 - 74", "75 - 84", 
                                 "85 and over")) %>%
  ggplot()+
  aes(x = year_quarter, 
      y = total_admissions_per_quarter_age, 
      group = age_group, 
      colour = age_group)+
  geom_point()+
  geom_line()+
  labs(x = "Yearly Quarters",
       y = "Total admissions",
       title = "(HB) Total Admissions per quarter across Scotland by age group",
       subtitle = "January, 2020 - February 2022",
       colour = "Age Group") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
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## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
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## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84
## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over


HB -Age- Weekly COVID admissions against the average admissions in 2018 & 19

covid_admissions_HB_AGE %>% 
  mutate(age_group = fct_relevel(age_group, 
                                 "Under 5", "5 - 14", "15 - 44", 
                                 "45 - 64", "65 - 74", "75 - 84", 
                                 "85 and over")) %>%
  group_by(age_group) %>% 
  ggplot()+
  aes(x = number_admissions, 
      y = average20182019, 
      colour = age_group)+
  geom_point() +
  labs(x = "Weekly number of admissions",
       y = "Average weekly admissions in 2018-2019",
       title = "(HB) Weekly admissions across Scotland in COVID times against 
       the equivalent average weekly admissions in previous years 
       (per age group)",
       subtitle = "COVID: January, 2020 - February, 2022 / 
       Previous years: 2018 - 2019",
       colour = "Age Group") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84
## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

The above but by month instead of EVERY input

covid_admissions_HB_AGE %>% 
  mutate(age_group = fct_relevel(age_group, 
                                 "Under 5", "5 - 14", "15 - 44", 
                                 "45 - 64", "65 - 74", "75 - 84", 
                                 "85 and over")) %>%
  group_by(age_group) %>% 
  ggplot()+
  aes(x = total_admissions_per_month_age, 
      y = admissions_2018_19_per_month_age, 
      colour = age_group)+
  geom_point() +
  labs(x = "Monthly number of admissions",
       y = "Average monthly admissions in 2018-2019",
       title = "(HB) Monthly admissions across Scotland in COVID times against 
       the equivalent average monthly admissions in previous years 
       (per age group)",
       subtitle = "COVID: January, 2020 - February, 2022 / 
       Previous years: 2018 - 2019",
       colour = "Age Group") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 45 - 64, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 75 - 84, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 85
## and over
## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84

## Warning: Unknown levels in `f`: Under 5, 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 -
## 84
## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

## Warning: Unknown levels in `f`: 5 - 14, 15 - 44, 45 - 64, 65 - 74, 75 - 84, 85
## and over

HB - Sex - Average Covid Admissions vs Average 2018 and 2019 admissions

  • remember that the agesex df has male, female, and all - male and female come under “All ages” but not specific age groups

  • Creating columns for monthly and quarterly totals - admissions, and average past ads

covid_admissions_HB_SEX <- covid_admissions_HB_agesex %>%
  filter(sex != "All")
# view(covid_admissions_HB_agesex)

covid_admissions_HB_SEX <- covid_admissions_HB_SEX %>% 
  group_by(sex, wk_ending_yr_month) %>% 
  mutate(total_admissions_per_month_sex = sum(number_admissions))

covid_admissions_HB_SEX <- covid_admissions_HB_SEX %>%  
  group_by(sex, wk_ending_yr_month) %>% 
  mutate(admissions_2018_19_per_month_sex = sum(average20182019))

covid_admissions_HB_SEX <- covid_admissions_HB_SEX %>% 
  group_by(sex, year_quarter) %>% 
  mutate(total_admissions_per_quarter_sex = sum(number_admissions))

covid_admissions_HB_SEX <- covid_admissions_HB_SEX %>%  
  group_by(sex, year_quarter) %>% 
  mutate(admissions_2018_19_per_quarter_sex = sum(average20182019))

Total admissions across HBs by gender for week ending:

covid_admissions_HB_SEX %>% 
  ggplot()+
  aes(x = wk_ending_yr_month, 
      y = total_admissions_per_month_sex,
      group = sex, 
      colour = sex)+
  scale_x_discrete(limits = dates) +
  geom_point()+
  geom_line()+
  labs(x = "Month and Year",
       y = "Total Admissions per Month",
       title = "Total Admissions across Health Boards in COVID Times per Month,
       by Sex", 
       fill = "Sex")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

Trying again - Total admissions across HBs by gender for week ending

covid_admissions_HB_SEX %>% 
  ggplot()+
  aes(x = wk_ending_yr_month, 
      y = total_admissions_per_month_sex,
      fill = sex)+
  scale_x_discrete(limits = dates) +
  geom_col(position = "dodge")+
  labs(x = "Month and Year",
       y = "Total Admissions per Month",
       title = "Total Admissions across Health Boards in COVID Times per Month,
       by Sex", 
       fill = "Sex")+  
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

By quarter

covid_admissions_HB_SEX %>% 
  ggplot()+
  aes(x = year_quarter, 
      y = total_admissions_per_quarter_sex,
      group = sex, 
      colour = sex)+
  geom_point()+
  geom_line()+
  labs(x = "Quarter and Year",
       y = "Total Admissions per Quarter",
       title = "(HB) Total Admissions across Health Boards in COVID Times per 
       Quarter, by Sex", 
       fill = "Sex")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

HEALTH BOARD - SIMD

HB - SIMD - Total Admissions

covid_admissions_HB_simd %>% 
  mutate(simd_quintile = fct_relevel(as.character(simd_quintile, 
                                  "1", "2", "3", "4", "5"))) %>%
  group_by(wk_ending_yr_month, simd_quintile) %>% 
  ggplot()+
  aes(x = number_admissions, 
      y = average20182019,
      colour = simd_quintile)+
  scale_x_discrete(limits = dates) +
  geom_point()+
  labs(x = "Weekly Number of Admissions in COVID Times",
       y = "Average Weekly Number of Admissions in 2018/2019",
       title = "(HB) Weekly number of admissions in COVID Times against the 
       average of the same week in 2018/2019 by SIMD Levels",
      colour = "SIMD Level:
       1 = Most Deprived
       5 = Least Deprived")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

So, creating monthly and quarterly total columns for each level of simd:

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  group_by(simd_quintile, wk_ending_yr_month) %>% 
  mutate(total_admissions_per_month_simd = sum(number_admissions))

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  group_by(simd_quintile, wk_ending_yr_month) %>% 
  mutate(admissions_2018_19_per_month_simd = sum(average20182019))

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  group_by(simd_quintile, year_quarter) %>% 
  mutate(total_admissions_per_quarter_simd = sum(number_admissions))

covid_admissions_HB_simd <- covid_admissions_HB_simd %>% 
  group_by(simd_quintile, year_quarter) %>% 
  mutate(admissions_2018_19_per_quarter_simd = sum(average20182019))

covid_admissions_HB_simd

PLOT total admissions per simd per month

covid_admissions_HB_simd %>% 
  mutate(simd_quintile = fct_relevel(as.character(simd_quintile, 
                                  "1", "2", "3", "4", "5"))) %>%
  group_by(wk_ending_yr_month, simd_quintile) %>% 
  ggplot()+
  aes(x = wk_ending_yr_month, 
      y = total_admissions_per_month_simd, 
      group = simd_quintile, 
      colour = simd_quintile)+
  scale_x_discrete(limits = dates) +
  geom_point()+
  geom_line()+
  labs(x = "Month and Year",
       y = "Total admissions",
       title = "(HB) Total admissions in COVID times per month by SIMD Level",
       subtitle = "January, 2020 - February 2022",
       colour = "SIMD Level:
       1 = Most Deprived
       5 = Least Deprived") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

SIMD - monthly AVERAGE admissions 2018 and 2019 against COVID admissions

covid_admissions_HB_simd %>% 
   mutate(simd_quintile = fct_relevel(as.character(simd_quintile, 
                                  "1", "2", "3", "4", "5"))) %>%
  group_by(simd_quintile) %>% 
  ggplot()+
  aes(x = total_admissions_per_month_simd, 
      y = admissions_2018_19_per_month_simd,
      colour = simd_quintile)+
  geom_point()+
  labs(x = "Monthly number of admissions in COVID times",
       y = "Average monthly admissions in 2018-2019",
       title = "(HB) Monthly admissions in COVID times against the equivalent monthly 
       average admissions in previous years, by SIMD Level",
       subtitle = "COVID: January, 2020 - February, 2022 / 
       Previous years: 2018 - 2019",
       colour = "SIMD Level:
       1 = Most Deprived
       5 = Least Deprived") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

BY QUARTER

covid_admissions_HB_simd %>% 
  mutate(simd_quintile = fct_relevel(as.character(simd_quintile, 
                                  "1", "2", "3", "4", "5"))) %>%
  ggplot()+
  aes(x = year_quarter, 
      y = total_admissions_per_quarter_simd,
      group = simd_quintile, 
      colour = simd_quintile)+
  geom_point()+
  geom_line()+
  labs(x = "Quarter and Year",
       y = "Total Admissions per Quarter",
       title = "(HB) Total Admissions across Health Boards in COVID Times per 
       Quarter, by SIMD", 
       colour = "SIMD Level:
       1 = Most Deprived
       5 = Least Deprived")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))

SIMD - quarterly AVERAGE admissions 2018 and 2019 against COVID admissions

covid_admissions_HB_simd %>% 
   mutate(simd_quintile = fct_relevel(as.character(simd_quintile, 
                                  "1", "2", "3", "4", "5"))) %>%
  group_by(simd_quintile) %>% 
  ggplot()+
  aes(x = total_admissions_per_quarter_simd, 
      y = admissions_2018_19_per_quarter_simd,
      colour = simd_quintile)+
  geom_point()+
  labs(x = "Quarterly number of admissions in COVID times",
       y = "Average quarterly admissions in 2018-2019",
       title = "(HB) Quarterly admissions in COVID times against the equivalent quarterly 
       average admissions in previous years, by SIMD Level",
       subtitle = "COVID: January, 2020 - February, 2022 / 
       Previous years: 2018 - 2019",
       colour = "SIMD Level:
       1 = Most Deprived
       5 = Least Deprived") +
  theme_bw()+
  theme(axis.text.x = element_text(angle = 45, hjust = 0.9))